{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Introduction_to_Weight_Quantization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction to Weight Quantization\n",
        "> Reducing the size of Large Language Models with 8-bit quantization\n",
        "\n",
        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
        "\n",
        "Companion notebook to execute the code from the following article: https://mlabonne.github.io/blog/intro_weight_quantization/"
      ],
      "metadata": {
        "id": "yG1VY-TJoxix"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WMVwLxdUzlq2"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "def absmax_quantize(X):\n",
        "    # Calculate scale\n",
        "    scale = 127 / torch.max(torch.abs(X))\n",
        "\n",
        "    # Quantize\n",
        "    X_quant = (scale * X).round()\n",
        "\n",
        "    # Dequantize\n",
        "    X_dequant = X_quant / scale\n",
        "\n",
        "    return X_quant.to(torch.int8), X_dequant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CE7XqWOR6oCa"
      },
      "outputs": [],
      "source": [
        "def zeropoint_quantize(X):\n",
        "    # Calculate value range (denominator)\n",
        "    x_range = torch.max(X) - torch.min(X)\n",
        "    x_range = 1 if x_range == 0 else x_range\n",
        "\n",
        "    # Calculate scale\n",
        "    scale = 255 / x_range\n",
        "\n",
        "    # Shift by zero-point\n",
        "    zeropoint = (-scale * torch.min(X) - 128).round()\n",
        "\n",
        "    # Scale and round the inputs\n",
        "    X_quant = torch.clip((X * scale + zeropoint).round(), -128, 127)\n",
        "\n",
        "    # Dequantize\n",
        "    X_dequant = (X_quant - zeropoint) / scale\n",
        "\n",
        "    return X_quant.to(torch.int8), X_dequant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lIYdn1woOS1n"
      },
      "outputs": [],
      "source": [
        "!pip install -q bitsandbytes>=0.39.0\n",
        "!pip install -q git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792,
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        },
        "id": "NTDg7uUOGBmS",
        "outputId": "cc48b090-31d1-41ae-ca5c-dbffcb67bcb6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7e1636dbc8944c49b375286b8d89fe4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so.11.0\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//172.28.0.1'), PosixPath('8013')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-20b5bv2xvtu9a --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7784bd5d3f1b4ac890a711c01bd653cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e047a8a2a0e4337b17f3e8612044967"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "681a9a78878945b7b6afb2d87b769146"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cea83a47549a4ddb91eae020d1cd943c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90cf2d52029d4392aaea970508506261"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model size: 510,342,192 bytes\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "import torch\n",
        "torch.manual_seed(0)\n",
        "\n",
        "# Set device to CPU for now\n",
        "device = 'cpu'\n",
        "\n",
        "# Load model and tokenizer\n",
        "model_id = 'gpt2'\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id).to(device)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "# Print model size\n",
        "print(f\"Model size: {model.get_memory_footprint():,} bytes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPI1EaimHyHm",
        "outputId": "977e9b34-9426-46a1-d6c2-da80884b7483"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original weights:\n",
            "tensor([[-0.4738, -0.2614, -0.0978,  ...,  0.0513, -0.0584,  0.0250],\n",
            "        [ 0.0874,  0.1473,  0.2387,  ..., -0.0525, -0.0113, -0.0156],\n",
            "        [ 0.0039,  0.0695,  0.3668,  ...,  0.1143,  0.0363, -0.0318],\n",
            "        ...,\n",
            "        [-0.2592, -0.0164,  0.1991,  ...,  0.0095, -0.0516,  0.0319],\n",
            "        [ 0.1517,  0.2170,  0.1043,  ...,  0.0293, -0.0429, -0.0475],\n",
            "        [-0.4100, -0.1924, -0.2400,  ..., -0.0046,  0.0070,  0.0198]])\n",
            "\n",
            "Absmax quantized weights:\n",
            "tensor([[-21, -12,  -4,  ...,   2,  -3,   1],\n",
            "        [  4,   7,  11,  ...,  -2,  -1,  -1],\n",
            "        [  0,   3,  16,  ...,   5,   2,  -1],\n",
            "        ...,\n",
            "        [-12,  -1,   9,  ...,   0,  -2,   1],\n",
            "        [  7,  10,   5,  ...,   1,  -2,  -2],\n",
            "        [-18,  -9, -11,  ...,   0,   0,   1]], dtype=torch.int8)\n",
            "\n",
            "Zero-point quantized weights:\n",
            "tensor([[-20, -11,  -3,  ...,   3,  -2,   2],\n",
            "        [  5,   8,  12,  ...,  -1,   0,   0],\n",
            "        [  1,   4,  18,  ...,   6,   3,   0],\n",
            "        ...,\n",
            "        [-11,   0,  10,  ...,   1,  -1,   2],\n",
            "        [  8,  11,   6,  ...,   2,  -1,  -1],\n",
            "        [-18,  -8, -10,  ...,   1,   1,   2]], dtype=torch.int8)\n"
          ]
        }
      ],
      "source": [
        "# Extract weights of the first layer\n",
        "weights = model.transformer.h[0].attn.c_attn.weight.data\n",
        "print(\"Original weights:\")\n",
        "print(weights)\n",
        "\n",
        "# Quantize layer using absmax quantization\n",
        "weights_abs_quant, _ = absmax_quantize(weights)\n",
        "print(\"\\nAbsmax quantized weights:\")\n",
        "print(weights_abs_quant)\n",
        "\n",
        "# Quantize layer using absmax quantization\n",
        "weights_zp_quant, _ = zeropoint_quantize(weights)\n",
        "print(\"\\nZero-point quantized weights:\")\n",
        "print(weights_zp_quant)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5i2N7HC9Mmn7"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from copy import deepcopy\n",
        "\n",
        "# Store original weights\n",
        "weights = [param.data.clone() for param in model.parameters()]\n",
        "\n",
        "# Create model to quantize\n",
        "model_abs = deepcopy(model)\n",
        "\n",
        "# Quantize all model weights\n",
        "weights_abs = []\n",
        "for param in model_abs.parameters():\n",
        "    _, dequantized = absmax_quantize(param.data)\n",
        "    param.data = dequantized\n",
        "    weights_abs.append(dequantized)\n",
        "\n",
        "# Create model to quantize\n",
        "model_zp = deepcopy(model)\n",
        "\n",
        "# Quantize all model weights\n",
        "weights_zp = []\n",
        "for param in model_zp.parameters():\n",
        "    _, dequantized = zeropoint_quantize(param.data)\n",
        "    param.data = dequantized\n",
        "    weights_zp.append(dequantized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FlM_jWwpHh34",
        "outputId": "0705932d-ec5a-4cb1-cc92-08072c014ee7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x3000 with 2 Axes>"
            ],
            "image/png": 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1116L1157Le6///74whe+ECeffHKn1gIQYY4F9A4ewgPaasGCBSkriM+aNSvj53G/j7aSSgHggDZt2hT33XdfUttHPvKRNoe3W6O2tjZpe/9XyLVGTk5OymrhzfsE6EytCS/deeed8c1vfjOWLFnSxdUBh7Lmc6LCwsI299F8bmaeBT1TR793pTumu4wHmzdvjhtvvDGeeOKJbJcC/H/pxpy2vlmp+bwlU2NObx4PgfbpzmNWR2zfvj3uuOOO+OUvf5ntUoBDgDkW0BusWbMmJRwe8V8P4f37v/97fPWrX40XX3wxC9UB3c3OnTvjf//v/53UdsIJJ8T48eMzfi73+2grK4gD0KI9e/bE7bffHrt3725qKy0tjU996lOdcr7mk472PHFbUFCQVK+JDNAd7Qsvfe5zn4s5c+ZkuxzgEJCpedaB+gR6hua/u81/t1ujq8aDfv36xXvf+9445phjYsyYMTF06NDo169f7NmzJ7Zt2xbLly+PZ599NpYtW9Z0TGNjY9xzzz0xcODAeN/73tcpdQGt153nID1pPAS6Rnces/bXp0+fmD59esyYMSPGjBkTw4YNi6Kioqivr4+qqqpYsWJF/PWvf42FCxcmBZt+97vfRf/+/ePjH/94xmsC2MccCzhU7HsIb9WqVfHZz3422+UAWdLY2Bg/+MEPYuvWrU1tRUVF8YUvfKFTztdTvrfSfQiIA5BWIpGIu+66K+kVlXl5efH1r389+vXr1ynn3Lt3b9J2fn7b/5lqfkxdXV2HagJoDeEloLvLxDyr+UUm8yzomXrKePD3f//3MWPGjLQroBQVFUVRUVGMHDkyPvCBD8S8efPixz/+cdTU1ETEu99n77777pg2bVoMGjQo47UBrdcZ13qa99lezceu7joeAl2nO49Z+5x99tkxadKk6N+/f9pzFxYWRllZWZx22mmxbNmyuPPOO6OysrJpn1/96lfx3ve+N8aMGZPRugD2MccCeioP4QHtce+998bChQuT2r7yla/E4Ycf3inn6ynX9+k+BMQButAvfvGLLnnV9fnnnx+f/OQnO9TH//k//ydeeumlpLaLLrooJk+e3KF+D6T5JKS+vr7NfTQ/pj1PywG0hfASdA89aZ6VDZmYZzW/6NSeFaCA7I9XnTEedMb3rpNPPrnV+86cOTMGDRoUN954Y9PF7D179sTDDz8cX/ziFzNeG9B63flaT/O5THcdD4Gu053HrH2OP/74Vu87efLkuP766+Oqq66KHTt2RMS716J+/etfx7/8y79ktC6AfcyxgJ7IQ3hAezzxxBPx2GOPJbWdc845ccopp3TaOd3vo60ExAFI8Zvf/CYef/zxpLbzzz8/PvKRj3TqeZuHK9uzukrzJ9vSBTaB1sl2eKmnEF4CegLzLGCf5r+77VkdpDuOBxMnToxzzjknHnzwwaa2559/Pi666KLIzc3NYmVwaOvOc5DeOh4C7dedx6z2GjZsWHz2s5+NH/3oR01tCxcujJ07d0ZJSUkWKwN6K3MsoCfyEB7QVs8//3zcc889SW2zZs2Kz3zmM5163t74vZXO5e4IAEn++Mc/xn333ZfUNnv27C4JbzafdOzZs6dNxycSCRMZoNvbF17a3/PPPx+NjY1Zqgg4FDSfE9XW1ra5j+ZzM/Ms6Jk6+r0r3THdZTyYM2dOUhh8586dsXr16ixWBKQbc/Z/FXdrNJ+3dFZAvDeNh0D7dOcxqyNOP/30GDBgQNN2IpGIRYsWZbEioDczxwIOBfsewtvfvofwgN7vlVdeibvuuivp++LMmTPj4osvjpycnE49t/t9tJUVxAG60LHHHpv2tUSZNmXKlHYd9/zzz8dPfvKTpLYzzjgjLrroogxUdXADBw5M2t66dWubjq+qqoqGhoam7ZycnC75+wZoqzlz5sTDDz/cFArfF14aP358liuDnqu7z7Oyrfk8a//XX7ZW82P2DxgArZft8aozxoPmfWZLSUlJHHnkkfHmm282tW3cuNEcC7JowIABkZOT03TDrKGhIaqqqmLQoEGt7qOz5iDN++lN4yHQPt15zOqI3NzcmDp1arz00ktNbRs3bsxiRUBvZo4FHCpOP/30uPfee6O6ujoi/ushvFNOOSXLlQGd6Y033ojbb789KZs0ffr0uOSSS7rkTZbu99FWAuIAXWj69Okxffr0bJeR1ssvv5zyhNuJJ54Y//iP/9jpT7jtM2LEiKTtLVu2tOn45vuXlpZGQUFBh+uCQ1W2w0u9mfASZF53nmd1Bx2dZ1VVVSW9pi4/Pz/KysoyUhscarI9Xg0dOjTy8vKaLmDX1dVFdXV1my4CNx9Dmo8x2TRkyJCkOda+m3RAdhQUFMThhx8eFRUVTW1btmxpU9iy+QICI0eOzEhtHZ0fRXRebUB2dOcxq6MOP/zwpG1zJKCzmGMBhwoP4cGhZ+XKlXHbbbcl3S+bNGlSfOtb34r8/K6J4brfR1sJiAMQixYtijvvvDPpCbdjjjkmvvnNb3bJE277NJ/IrF+/vk3HN9/fBSPomGyHl3o74SWgKzWfF23evDnq6+tbfcFqw4YNSdtlZWWRl5eXsfqArrPvgu/+N6zWr18fU6dObdXxe/fujfLy8qS27vTdq/m4Vl9fn6VKgH1GjBiRFLZcv359mx6O7azrPfsWFqirq4uId1+vW1FREaWlpa06vqKiIumVvH379o0hQ4ZkpDYge7rrmNVRzb+/7X8vACCTzLGAQ4mH8ODQsXbt2vjOd74TtbW1TW1HHnlkfPvb347CwsIuq8P9Ptqq61J/AHRLy5Yti+9+97tJT4hNmTIl/vmf/7nLnnDbZ/To0UkTj4qKiti2bVurj1++fHnS9nve856M1QaQacJLQFcaNGhQ0qp3e/fujdWrV7f6+GXLliVtjxkzJkOVAdnQ/He4+XepA1m9enXS98fDDjusW73ue/v27UnbXo8J2dd8zFmxYkWrj922bVtSUDMvLy9GjRqVkbpycnJSrh21ZTxMdx2qq97CB3Se7jpmdZQ5EtBVzLGAQ4mH8ODQsHHjxrj55pujpqamqW3kyJFx1VVXRVFRUZfW4n4fbSUgDnAIW716dfzrv/5r0pP448aNiyuvvDIKCgq6vJ5+/fqlrFq3aNGiVh2bSCTi9ddfT2o7/vjjM1YbQKa5MQd0tWOPPTZpu7XzrIhImWcdd9xxGakJyI6OjAfN9+1O48HevXuT3tASkbqSE9D1mo8Tr7/+eiQSiVYd+9prryVtH3XUURldlam3jodA+3XnMasjmocArMYLdCZzLOBQ4V4f9H4VFRVx0003RVVVVVPb0KFD45prrsna77z7fbSFgDjAIWr9+vVxyy23xK5du5raRo8eHf/9v//3Ln/CbX/NJx9PP/10q45bvHhx0mvOBw4c2KZXfwJ0JeElIBuaPzz3zDPPtCrosGnTpliyZEnTdl5eXsrFJ6BnOfbYY5NWOFqyZEls3rz5oMclEol49tlnk9q604O5f/3rX5MegO7Tp09MmjQpixUBERETJ06M/v37N21v3rw5Fi9e3Kpjn3nmmaTtTI85zft76aWXkl4V3JLdu3fHiy++mNR2wgknZLQ2IDu685jVXunmekcffXSWqgEOBeZYwKHCQ3jQu23bti1uvPHG2Lp1a1Pb4MGD49prr43BgwdnrS73+2gLAXGAQ1B5eXncdNNNsWPHjqa2YcOGxTXXXJN08Tsb3ve+90Xfvn2btpcuXRpvvPHGAY9JJBLxwAMPJLW9//3vj9xc/8wB3ZPwEpANxxxzTNIF6oqKipQAQzoPPPBA0oWlE088MasPFAIdV1JSknSTPd13qnSeeeaZqKioaNouLS2N6dOnd0qNbbV9+/b49a9/ndQ2ffr0pO+XQHbk5ubGrFmzktoefPDBg964ev3112Pp0qVN2/369YuTTz45o7W95z3viXHjxjVt19bWxqOPPnrQ4x599NGk73QTJkyIUaNGZbQ2IDu685jVHrW1tfHzn/88qe2II46IsrKyLFUEHArMsYBDgYfwoHfbuXNn3HzzzUm/5wMGDIirr746hg4dmsXK3O+jbSTnAA4xlZWVcdNNN8W2bdua2g4//PC49tprY9CgQRk91+LFi+OTn/xk0v8PZuDAgfHRj340qe3uu++OysrKFo955JFHki6+FxUVxTnnnNP+wgE6kfASkAnl5eUp86z936aSTp8+feITn/hEUtsvf/nLWL9+fYvHPP/88/GXv/ylaTs3N7dVczqg+/vkJz8ZOTk5Tdt//vOf4/nnn29x//Xr18cvf/nLpLbzzjsv8vPzD3ieto5X27Zti/vvvz927tzZyj/Ju+e45ZZbkr435uTkxAUXXNDqPoDONXfu3CgsLGzaXrJkyQFDQpWVlXH33Xcntc2ePfugr+5tPt60ZtXfT33qU0nbv/nNb5JWU2ouXe0XXnjhQc8D9Bzddcz6+c9/fsDr5M1VV1fHbbfdFmvXrk05L0BbmGMBJPMQHvRuu3fvjltuuSXefvvtprbi4uK4+uqrM/7wmvt9dLYD38EBoFfZs2dP3HLLLUlPuOXm5sbHP/7x2LhxY2zcuLFN/U2ePDkKCgoyXWbMnTs3nnvuudi+fXtEvDshuuaaa+ILX/hCHHfccU0hhq1bt8ZDDz0UTz31VNLx5557bpSUlGS8LqB3Ky8vj6997WtJbT/84Q9bfAJ427Zt8cc//jHmzJnT6jGnvLw8vvvd7wovQS+3aNGitO3NL8zU1NS0uG9ZWVmnXEz+wAc+EP/5n//ZdFGrpqYmrr322vj85z8fp556auTl5UXEuysjPPbYY/HII48kHf/BD34wRowYkfG6gK43atSoOPPMM5O+T/3gBz+IDRs2xFlnndU0v6mvr4/nn38+7rnnnqipqWna9z3veU+cccYZGa9r79698eCDD8Zjjz0Wxx9/fJx44okxYcKEtK/s3LRpUzz77LPx+9//Pnbt2pX02ezZs2Ps2LEZrw9onwEDBsQnPvGJ+NW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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "# Flatten weight tensors\n",
        "weights = np.concatenate([t.cpu().numpy().flatten() for t in weights])\n",
        "weights_abs = np.concatenate([t.cpu().numpy().flatten() for t in weights_abs])\n",
        "weights_zp = np.concatenate([t.cpu().numpy().flatten() for t in weights_zp])\n",
        "\n",
        "# Set background style\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "# Create figure and axes\n",
        "fig, axs = plt.subplots(2, figsize=(10,10), dpi=300, sharex=True)\n",
        "\n",
        "# Plot the histograms for original and zero-point weights\n",
        "axs[0].hist(weights, bins=150, alpha=0.5, label='Original weights', color='blue', range=(-2, 2))\n",
        "axs[0].hist(weights_abs, bins=150, alpha=0.5, label='Absmax weights', color='red', range=(-2, 2))\n",
        "\n",
        "# Plot the histograms for original and absmax weights\n",
        "axs[1].hist(weights, bins=150, alpha=0.5, label='Original weights', color='blue', range=(-2, 2))\n",
        "axs[1].hist(weights_zp, bins=150, alpha=0.5, label='Zero-point weights', color='green', range=(-2, 2))\n",
        "\n",
        "# Add grid\n",
        "for ax in axs:\n",
        "    ax.grid(True, linestyle='--', alpha=0.6)\n",
        "\n",
        "# Add legend\n",
        "axs[0].legend()\n",
        "axs[1].legend()\n",
        "\n",
        "# Add title and labels\n",
        "axs[0].set_title('Comparison of Original and Absmax Quantized Weights', fontsize=16)\n",
        "axs[1].set_title('Comparison of Original and Zeropoint Quantized Weights', fontsize=16)\n",
        "\n",
        "for ax in axs:\n",
        "    ax.set_xlabel('Weights', fontsize=14)\n",
        "    ax.set_ylabel('Count', fontsize=14)\n",
        "    ax.yaxis.set_major_formatter(ticker.EngFormatter()) # Make y-ticks more human readable\n",
        "\n",
        "# Improve font\n",
        "plt.rc('font', size=12)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQS5PyPW_L8v",
        "outputId": "1b4ee2b5-4097-421e-9e96-f9ae83221137"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model:\n",
            "I have a dream, and it is a dream I believe I would get to live in my future. I love my mother, and there was that one time I had been told that my family wasn't even that strong. And then I got the\n",
            "--------------------------------------------------\n",
            "Absmax model:\n",
            "I have a dream to find out the origin of her hair. She loves it. But there's no way you could be honest about how her hair is made. She must be crazy.\n",
            "\n",
            "We found a photo of the hairstyle posted on\n",
            "--------------------------------------------------\n",
            "Zeropoint model:\n",
            "I have a dream of creating two full-time jobs in America—one for people with mental health issues, and one for people who do not suffer from mental illness—or at least have an employment and family history of substance abuse, to work part\n"
          ]
        }
      ],
      "source": [
        "def generate_text(model, input_text, max_length=50):\n",
        "    input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)\n",
        "    output = model.generate(inputs=input_ids,\n",
        "                            max_length=max_length,\n",
        "                            do_sample=True,\n",
        "                            top_k=30,\n",
        "                            pad_token_id=tokenizer.eos_token_id,\n",
        "                            attention_mask=input_ids.new_ones(input_ids.shape))\n",
        "    return tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "\n",
        "# Generate text with original and quantized models\n",
        "original_text = generate_text(model, \"I have a dream\")\n",
        "absmax_text   = generate_text(model_abs, \"I have a dream\")\n",
        "zp_text       = generate_text(model_zp, \"I have a dream\")\n",
        "\n",
        "print(f\"Original model:\\n{original_text}\")\n",
        "print(\"-\" * 50)\n",
        "print(f\"Absmax model:\\n{absmax_text}\")\n",
        "print(\"-\" * 50)\n",
        "print(f\"Zeropoint model:\\n{zp_text}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "py3nuq-WOhwn",
        "outputId": "dc738c29-170e-4b6e-e3e0-2fe0c7c34e70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original perplexity: 15.53\n",
            "Absmax perplexity:   17.92\n",
            "Zeropoint perplexity: 17.97\n"
          ]
        }
      ],
      "source": [
        "def calculate_perplexity(model, text):\n",
        "    # Encode the text\n",
        "    encodings = tokenizer(text, return_tensors='pt').to(device)\n",
        "\n",
        "    # Define input_ids and target_ids\n",
        "    input_ids = encodings.input_ids\n",
        "    target_ids = input_ids.clone()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model(input_ids, labels=target_ids)\n",
        "\n",
        "    # Loss calculation\n",
        "    neg_log_likelihood = outputs.loss\n",
        "\n",
        "    # Perplexity calculation\n",
        "    ppl = torch.exp(neg_log_likelihood)\n",
        "\n",
        "    return ppl\n",
        "\n",
        "ppl     = calculate_perplexity(model, original_text)\n",
        "ppl_abs = calculate_perplexity(model_abs, absmax_text)\n",
        "ppl_zp  = calculate_perplexity(model_zp, absmax_text)\n",
        "\n",
        "print(f\"Original perplexity: {ppl.item():.2f}\")\n",
        "print(f\"Absmax perplexity:   {ppl_abs.item():.2f}\")\n",
        "print(f\"Zeropoint perplexity: {ppl_zp.item():.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_4gVonK0XDE",
        "outputId": "df61f21e-1d3c-4ba9-9e96-4226f2532435"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at gpt2 and are newly initialized: ['lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model size: 176,527,896 bytes\n"
          ]
        }
      ],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "model_int8 = AutoModelForCausalLM.from_pretrained(model_id,\n",
        "                                             device_map='auto',\n",
        "                                             load_in_8bit=True,\n",
        "                                             )\n",
        "print(f\"Model size: {model_int8.get_memory_footprint():,} bytes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "XpghDRDlY_6f",
        "outputId": "eda8f6c8-c53a-4d94-a447-4ec35ed0a8bf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x1500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "# Flatten weight tensors\n",
        "weights_int8 = [param.data.clone() for param in model_int8.parameters()]\n",
        "weights_int8 = np.concatenate([t.cpu().numpy().flatten() for t in weights_int8])\n",
        "\n",
        "# Set background style\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "# Create figure and axis\n",
        "fig, ax = plt.subplots(figsize=(10,5), dpi=300)\n",
        "\n",
        "# Plot the histograms\n",
        "ax.hist(weights, bins=150, alpha=0.5, label='Original weights',\n",
        "        color='blue', range=(-2, 2))\n",
        "ax.hist(weights_int8, bins=150, alpha=0.5, label='LLM.int8() weights',\n",
        "        color='red', range=(-2, 2))\n",
        "\n",
        "# Add grid\n",
        "ax.grid(True, linestyle='--', alpha=0.6)\n",
        "\n",
        "# Add legend\n",
        "ax.legend()\n",
        "\n",
        "# Add title and labels\n",
        "ax.set_title('Comparison of Original and Dequantized Weights', fontsize=16)\n",
        "ax.set_xlabel('Weights', fontsize=14)\n",
        "ax.set_ylabel('Count', fontsize=14)\n",
        "plt.gca().yaxis.set_major_formatter(ticker.EngFormatter())\n",
        "\n",
        "# Improve font\n",
        "plt.rc('font', size=12)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoDwrdDMZPuR",
        "outputId": "7db34a58-b2cd-423e-fa83-ce5e531934e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model:\n",
            "I have a dream, and it is a dream I believe I would get to live in my future. I love my mother, and there was that one time I had been told that my family wasn't even that strong. And then I got the\n",
            "--------------------------------------------------\n",
            "LLM.int8() model:\n",
            "I have a dream. I don't know what will come of it, but I am going to have to look for something that will be right. I haven't thought about it for a long time, but I have to try to get that thing\n"
          ]
        }
      ],
      "source": [
        "# Generate text with quantized model\n",
        "text_int8 = generate_text(model_int8, \"I have a dream\")\n",
        "\n",
        "print(f\"Original model:\\n{original_text}\")\n",
        "print(\"-\" * 50)\n",
        "print(f\"LLM.int8() model:\\n{text_int8}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XGQ0N9_KyoA",
        "outputId": "d772d77e-50a5-4b28-90b1-0b407962cdae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perplexity (original):   15.53\n",
            "Perplexity (LLM.int8()): 7.93\n"
          ]
        }
      ],
      "source": [
        "print(f\"Perplexity (original):   {ppl.item():.2f}\")\n",
        "\n",
        "ppl = calculate_perplexity(model_int8, text_int8)\n",
        "print(f\"Perplexity (LLM.int8()): {ppl.item():.2f}\")"
      ]
    }
  ],
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